“Not the limit of our imagination”: exploring student advocacy discourses in support of universal higher education
Bibliographic record
Abstract
Though higher education should be accessible to all on the basis of merit, as per international human rights law, neoliberal policies enacted by provincial governments increasingly worsen financial access to post-secondary education (PSE) in Canada. The ongoing defunding of institutions and student aid are in direct conflict with this right, and represent a significant social justice concern. The Canadian Federation of Students (CFS), the largest centralized student advocacy body in Canada, fights against neoliberal policies that produce and reinforce marginalization of low-income students through a collective social movement in favor of universal PSE. Their lobbying and advocacy are informed by discourses performed on their website and by their members; this study investigates how these discourse frame and inform the CFS’s movement towards equitable realization of the right to equitable PSE. Social semiotics (Hodge & Kress, 1988) and discourse analysis (Jørgensen & Phillips, 2002) are used as methodological frameworks to unpack how the discourse performed by the CFS in their electronic publications and by their members further their collective advocacy. Discussion of how these discourses could be used to improve PSE accessibility and inclusivity in Canada, along with suggested areas of future research, conclude this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.076 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".